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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2016

Comparisons Between Pretest Prediction and Flight Test Data of Aerodynamic Loading for EFT-1

Exploration Flight Test One (EFT-1) was an incredible milestone in the development NASA's Orion spacecraft. It incorporated hundreds of articles of flight test instrumentation and returned with a wealth of data. Aerodynamic surface pressures were collected during launch vehicle ascent and capsule reentry and descent. These discrete surface pressure measurements enable comparisons to computational results and ground test data. This paper details the comparisons between pre-test predictions and flight test data for the Orion MPCV Crew Module (CM) and Launch Abort Tower (LAT) during all phases of flight. Regions with strong comparisons, poor predictions, and lessons learned are discussed. 38 pressure measurements were made on the LAT during ascent. Nine of the gauges were Honeywell PPTs and the remainder were Kulite pressure transducers. In order to address bias in the Kulites, a two-point linear calibration was used and the details are discussed. Results from the flight are compared to existing database products. 44 pressure measurements were made on the CM during reentry and descent. Nine of the gauges were Honeywell PPTs and the remainder were Kulite pressure transducers. In order to address bias in the Kulites, a tare was made against the vacuum measurements as described below. Once the bias was removed from the gauges, comparisons between predicted loading and the measured results are compared.

Schwing, Alan M.↗

NASA NTRS2016

Assessment of JVX Proprotor Performance Data in Hover and Airplane-Mode Flight Conditions

A 0.656-scale V-22 proprotor, the Joint Vertical Experimental (JVX) rotor, was tested at the NASA Ames Research Center in both hover and airplane-mode (high-speed axial flow) flight conditions, up to an advance ratio of 0.562 (231 knots). This paper examines the two principal data sets generated by those tests, and includes investigations of hub spinner tares, torque/thrust measurement interactions, tunnel blockage effects, and other phenomena suspected of causing erroneous measurements or predictions. Uncertainties in hover and high-speed data are characterized. The results are reported here to provide guidance for future wind tunnel tests, data processing, and data analysis.

Proprotor↗

NASA NTRS2017

Wind Tunnel Strain-Gage Balance Calibration Data Analysis Using a Weighted Least Squares Approach

A new approach is presented that uses a weighted least squares fit to analyze wind tunnel strain-gage balance calibration data. The weighted least squares fit is specifically designed to increase the influence of single-component loadings during the regression analysis. The weighted least squares fit also reduces the impact of calibration load schedule asymmetries on the predicted primary sensitivities of the balance gages. A weighting factor between zero and one is assigned to each calibration data point that depends on a simple count of its intentionally loaded load components or gages. The greater the number of a data point's intentionally loaded load components or gages is, the smaller its weighting factor becomes. The proposed approach is applicable to both the Iterative and Non-Iterative Methods that are used for the analysis of strain-gage balance calibration data in the aerospace testing community. The Iterative Method uses a reasonable estimate of the tare corrected load set as input for the determination of the weighting factors. The Non-Iterative Method, on the other hand, uses gage output differences relative to the natural zeros as input for the determination of the weighting factors. Machine calibration data of a six-component force balance is used to illustrate benefits of the proposed weighted least squares fit. In addition, a detailed derivation of the PRESS residuals associated with a weighted least squares fit is given in the appendices of the paper as this information could not be found in the literature. These PRESS residuals may be needed to evaluate the predictive capabilities of the final regression models that result from a weighted least squares fit of the balance calibration data.

calibration analysis↗

NASA NTRS2017

Transonic Drag Reduction Through Trailing-Edge Blowing on the FAST-MAC Circulation Control Model

A third wind tunnel test of the FAST-MAC circulation control semi-span model was completed in the National Transonic Facility at the NASA Langley Research Center where the model was configured for transonic testing of the cruise configuration with 0deg flap detection to determine the potential for transonic drag reduction with the circulation control blowing. The model allowed independent control of four circulation control plenums producing a high momentum jet from a blowing slot near the wing trailing edge that was directed over a 15% chord simple-hinged ap. Recent upgrades to transonic semi-span flow control testing at the NTF have demonstrated an improvement to overall data repeatability, particularly for the drag measurement, that allows for increased confidence in the data results. The static thrust generated by the blowing slot was removed from the wind-on data using force and moment balance data from wind-o thrust tares. This paper discusses the impact of the trailing-edge blowing to the transonic aerodynamics of the FAST-MAC model in the cruise configuration, where at flight Reynolds numbers, the thrust-removed corrected data showed that an overall drag reduction and increased aerodynamic efficiency was realized as a consequence of the blowing.

Chan, David T.↗

NASA NTRS2019

High-Speed Wind Tunnel Tests of a Full-Scale Proprotor on the Tiltrotor Test Rig

The Tiltrotor Test Rig (TTR) is a NASA project, joint with the U.S. Army and Air Force, to develop a new, large scale proprotor test system for the National Full-Scale Aerodynamics Complex (NFAC). The first wind-tunnel entry was completed in November 2018 with a modern, 26-ft diameter proprotor. The primary purpose was to complete the development of the TTR, including systems integration with the NFAC. The TTR and rotor were tested up to 273 knots in axial flow. This is the highest airspeed ever achieved by a full-scale proprotor in any wind tunnel. Extensive conversion-mode data were also acquired, and hover/climb conditions were explored. Additional testing included aerodynamic tares, motor tests, thermal tests, modal vibration tests, and other checkout activities. This paper summarizes the results of the test, including examples of the most significant data.

Wind Tunnel Tests↗

NASA NTRS2019

Dynamic Testing of a High-Specific-Torque Concentric Magnetic Gear

This paper presents initial measurements of the efficiency of NASA's 2nd magnetic gear prototype. A detailed discussion of the test rig's design and capabilities was presented, along with a thorough uncertainty analysis. The reported uncertainties are 95% confidence intervals that include the effects of temperature and parasitic loads. The prototype's response was measured at output speeds between 124 rpm and 744 rpm for a controlled output torque of 10 Nm (8% of the prototype's maximum torque). After correcting for tare losses, the prototype's efficiency was found to decrease from 90.0% to 83.0% as speed increased. If the efficiency is extrapolated to a typical operating condition (85% of maximum torque) using the good assumption that energy loss is approximately independent of the transmitted torque, the expected efficiency would be 99.0% to 98.4%, which exceeds the state of the art for these speeds.

magnetic gears↗

NASA NTRS2019

Comparison of Electrical Output Format Options for the Analysis of Strain-Gage Balance Calibration Data

Fundamental characteristics of three gage output formats are discussed that may be used to both interpret and analyze wind tunnel strain-gage balance data. The first output format uses raw outputs, i.e., absolute voltage measurements, for the analysis. This choice requires an intercept term in the regression model of the outputs if the Iterative Method is chosen for the load prediction. Raw outputs can be used for the analysis of all known output characteristics as long as the Iterative Method is applied. However, raw outputs cannot be used to analyze data of a balance with bi-directional outputs if the Non-Iterative Method is chosen for the analysis. The second output format, i.e., difference type 1, uses the difference between raw outputs and the natural zeros of the balance gages for the analysis. In this case, the use of the intercept term becomes optional. Analysis results for difference type 1 will exactly match analysis results obtained by using raw outputs as long as identical math models are used for the regression analysis. In addition, difference type 1 may be used to analyze data of a balance with bi directional outputs if an analyst prefers to process data using the Non-Iterative Method. The third output format, i.e., difference type 2, uses the difference between raw outputs and the outputs of the zero load point of a load series for the data analysis. The application of this format is only recommended if the maximum magnitude of all tare loads of a given balance calibration data set is less than two percent of capacity. Data from the calibration of a force balance is used to illustrate the application of the three gage output formats.

Wind Tunnel Strain-Gage Balance↗

NASA NTRS2019

Dynamic Testing of a High-Specific-Torque Concentric Magnetic Gear

This presentation presents measurements of the efficiency of NASA's 2nd magnetic gear prototype. A detailed discussion of the test rig used to make these measurements was presented, including a thorough uncertainty analysis. The reported uncertainties are 95% confidence intervals that include the effects of temperature and parasitic loads. The prototype's response was measured at output speeds between 124 rpm and 744 rpm for a controlled output torque of 10 Nm (8% of the prototype's maximum torque). After correcting for tare losses, the prototype's efficiency was found to decrease from 90.0% to 83.0% as speed increased. If the efficiency is extrapolated to a typical operating condition (85% of maximum torque) using the good assumption that energy loss is approximately independent of the transmitted torque,the expected efficiency would be 99.0% to 98.4%, which exceeds the state of the art for these speeds.

Scheidler, Justin↗

NASA NTRS2019

High Speed Testing of a High Efficiency Concentric Magnetic Gear

Scheidler, Justin↗

NASA NTRSDate not supplied

Comparison of two Calibrations of NASA's MC60 Five-component Semi-span Balance

Two separate manual calibrations of NASA’s MC60 five–component semi–span balance were compared. The calibrations were performed in 1999 and 2019using CALSPAN Force Measurement System’s Large Load Rig. The Iterative Method was chosen as the load prediction algorithm and data reduction matrices were generated from both calibration data sets. Then, the applied calibration load schedules, the standard deviations of the calibration load residuals, the prime sensitivities of the five gages, and the maximum outputs at capacity of the five gages were compared. In addition, the calibration data of 1999 was used as check load data for the data reduction matrix that was obtained from the 2019 data. Overall, the agreement between the data analysis results is very good considering the facts that two different load schedules were used for the calibrations, different people performed the calibrations, different instrumentation was used to measure the loads and the outputs, the Large Load Rig was moved to a different site in between the calibrations, and no tare load corrections could be applied to the 1999 data set. Nevertheless, the calibration data of 2019 is clearly the better data set. This observation is no surprise considering that CALSPAN Force Measurement Systems made significant investments in calibration hardware and process improvements during the last 20 years.

wind tunnel balance↗

NASA NTRSDate not supplied

Model Preparation Areas for Propulsion Airframe Integration Testing at NASA Langley Research Center

Currently there are three major NASA Langley facilities that perform PAI testing. These include the National Transonic Facility, the 14’x22’ Low Speed Wind tunnel, and the Unitary Plan Wind Tunnel. While each of these facilities have their own model preparation areas, the model preparation area located at the NTF complex that focuses on blowing systems with high mass flow requirements support all of the LaRC facilities. The application of a Propulsion Airframe Integration requires special attention to the air delivery system interaction with the model. These interactions typically focus on the tares across the balance associated with inlet and nozzle performance which are related to the mass flow through the system and the efficiency of the inlet and nozzle geometries. While this paper does not characterize any specific propulsion system, it does focus on the mass flow and pressures requirement needed for pretesting such systems prior to wind tunnel installation. Weight flow boundaries for the air delivery system were identified to range from 0.1 to 20 lbm/sec.

Model Preparation Area↗

NASA NTRSDate not supplied

Model Preparation Areas for Propulsion Airframe Integration Testing at the NASA Langley Research Center

Currently, there are three major NASA Langley facilities that perform Propulsion Airframe Integration (PAI) testing. These include the National Transonic Facility (NTF), the 14- by 22-Foot Subsonic Tunnel, and the 4-Foot Supersonic Unitary Plan Wind Tunnel. While each of these facilities have their respective model preparation areas, one model preparation area, located at the NTF complex, focuses on high mass flow blowing systems in support of other Langley Research Center facilities. The application of PAI requires special attention to the air delivery system interaction with the model. These interactions focus on the tares across the force and moment balance. The interactions are a result of the mass flow and pressure of the air through the system and the efficiency of the inlet and nozzle geometries. While this paper does not characterize any specific propulsion system, it does focus on the mass flow and pressure requirements for risk-reduction planning of such systems prior to installation in a designated wind tunnel. An example of model and nozzle sizing will be presented for the Tail Cone Thruster (TCT) variant of the Common Research Model (CRM).

PAI↗

NASA NTRS2022

Wind Tunnel Performance Tests of a Full-Scale Proprotor on the Tiltrotor Test Rig

The Tiltrotor Test Rig (TTR) is a new, large-scale proprotor test system for the National Full-Scale Aerodynamics Complex (NFAC). The TTR was jointly developed by NASA, the U. S. Army and the U. S. Air Force. The first wind-tunnel entry was completed in November 2018 with a modern, 26-ft diameter proprotor. The primary purpose was to complete the development of the TTR, including systems integration with the NFAC. The TTR and rotor were tested up to 273 knots in axial flow, which is the highest airspeed ever achieved by a full-scale proprotor in any wind tunnel. The entry constitutes the first full-scale wind-tunnel test of a gimballed, coning-flexure rotor. Extensive conversion-mode data were also acquired, and hover/climb conditions were explored. Additional testing included aerodynamic tares, motor tests, thermal tests, modal vibration tests, and other checkout activities. This report summarizes the results of the test, including examples of the most significant data.

Tiltrotor Test Rig↗

NASA NTRS2021

Evaluation, Analysis, and Application of Internal Strain-Gage Balance Data

Experimental processes, analytical methods, and numerical algorithms are described that may be used to predict the forces and moments of an internal strain-gage balance during a wind tunnel test. First, the control volume model of a strain-gage balance and the concepts of load state, load space, and output space are introduced. These important abstractions provide a better understanding of fundamental characteristics of different balance load prediction approaches. Then, the description of strain-gage balance data and the definition of the primary gage sensitivity is discussed. Afterwards, basic elements of the calibration of a typical six-component balance are reviewed. Two fundamentally different balance load prediction methods, the processing of check loads, and related topics are also discussed. Three real-world balance data examples are reviewed in great detail to illustrate typical analysis results for a variety of strain-gage balance designs. Finally, important observations are summarized and recommendations are provided. Additional information and detailed mathematical derivations can be found in the appendices of the document. They include the following topics: balance terminology, definitions of important statistical metrics, balance axis system conventions, balance load transformations, the combined load diagram, electrical output format options, bi-directional gage output characteristics, determination of the natural zeros, derivation of two balance load prediction methods, description of two tare load iteration algorithms, modeling of balance temperature effects, basics of three-component moment balances, definition of the percent contribution, detection of linear and near-linear dependencies in balance calibration data, a regression model term selection algorithm, and other related topics.

wind tunnel test↗

NASA NTRS2024

Evaluation, Analysis, and Application of Internal Strain-Gage Balance Data

Experimental processes, analytical methods, and numerical algorithms are described that may be used to predict the forces and moments of an internal strain–gage balance during a wind tunnel test. First, the control volume model of a strain–gage balance and the concepts of load state, load space, and output space are introduced. These important abstractions provide a better understanding of fundamental characteristics of different balance load prediction approaches. Then, the description of strain–gage balance data and the definition of the primary bridge sensitivity are discussed. Afterwards, basic elements of the calibration of a typical six–component balance are reviewed. Two fundamentally different balance load prediction methods, the processing of check loads, and related topics are also discussed. Three real–world balance data examples are reviewed in great detail to illustrate typical analysis results for a variety of strain–gage balance designs. Finally, important observations are summarized and recommendations are provided. – Additional information and detailed mathematical derivations can be found in the appendices of the document. They include the following topics: balance terminology, definitions of important statistical metrics, balance axis system conventions, balance load transformations, the combined load diagram, electrical output format options, bi–directional output characteristics, determination of the natural zeros, derivation of two balance load prediction methods, description of two tare load iteration algorithms, modeling of balance temperature effects, basics of three–component moment balances, definition of the percent contribution, detection of linear and near–linear dependencies in balance calibration data, a regression model search algorithm, balance interactions, and other related information.

strain-gage balance↗

NASA NTRSDate not supplied

Statistical Wind-Tunnel Experimentation Advancements for eVTOL Aircraft Aero-Propulsive Model Development

Compared to many conventional aircraft, electric vertical takeoff and landing (eVTOL) vehicle designs present greater aero-propulsive complexity and many interacting factors requiring development of novel testing and model development strategies. This paper describes several advances in statistically-rigorous experiment design methods for wind-tunnel testing of eVTOL aircraft enabling accurate aero-propulsive model development. The techniques are applied to a subscale tilt-rotor eVTOL aircraft configuration built at NASA Langley Research Center that has 24 independent control effectors. The general testing approach, leveraging design of experiments and response surface methodology, is outlined and followed by a detailed description of the experiment design and modeling approach employed for different phases of the wind-tunnel testing. A new approach for gravitational tare modeling is developed and validated, which reduces the required wind-tunnel test time by nearly 50%. A mostly automated trim envelope determination approach is formulated to rapidly determine the aircraft flight envelope to inform transition testing for aero-propulsive model development. Efficient powered-airframe characterization tests are conducted using a nested I-optimal design approach formulated in previous simulation-based testing. The collective wind-tunnel testing approach facilitates rapid characterization of eVTOL aircraft configurations and produces accurate aero-propulsive models that can be used in flight dynamics simulations.

design of experiments↗

NASA NTRS2024

Tiltrotor Test Rig Wind-Tunnel Test Data Catalog

The Tiltrotor Test Rig (TTR) is a large-scale proprotor test system for the National Full-Scale Aerodynamics Complex (NFAC). The first wind-tunnel entry was completed in November 2018 with a modern, 26-ft diameter proprotor. The rotor tested was the Bell Model 699, an AW609 rotor modified specifically for wind-tunnel testing. The primary purpose was to complete the development of the TTR, including systems integration with the NFAC. Testing included axial flow up to 273 knots, conversion mode from 30 to 100 deg nacelle angle, and hover/vertical climb conditions. Additional testing included aerodynamic tares, motor tests, thermal tests, modal vibration tests, and other checkout activities. This report lists the test conditions achieved and the types of data acquired and saved in the database.

NFAC↗

NASA NTRS1983

Standard free energy of formation of iron iodide

An experiment is reported where silver iodide is used to determine the standard free energy of formation of iron iodide. By using silver iodide as a solid electrolyte, a galvanic cell, Ag/AgI/Fe-FeI2, is formulated. The standard free energy of formation of AgI is known, and hence it is possible to estimate the standard free energy of formation of FeI2 by measuring the open-circuit emf of the above cell as a function of temperature. The free standard energy of formation of FeI2 determined by this method is -38784 + 24.165T cal/mol. It is estimated that the maximum error associated with this method is plus or minus 2500 cal/mol.

Khandkar, A.↗